Practice guidelines for endoscopic ultrasound-guided celiac plexus neurolysis
Bibliographic record
Abstract
The objective of guideline was to provide clear and relevant consensus statements to form a practical guideline for clinicians on the indications, optimal technique, safety and efficacy of endoscopic ultrasound guided celiac plexus neurolysis (EUS-CPN).Six important clinical questions were determined regarding EUS-CPN. Following a detailed literature review, 6 statements were proposed attempting to answer those questions. A group of expert endosonographers convened in Chicago, United States (May 2016), where the statements were presented and feedback provided. Subsequently a consensus group of 35 expert endosonographers voted based on their individual level of agreement. A strong recommendation required 80% voter agreement. The modified GRADE (Grading of Recommendations Assessment, Development, and Evaluation) criteria were used to rate the strength of recommendations and the quality of evidence.Eighty percent agreement was reached on 5 of 6 consensus statements, 79.4% agreement was reached on the remaining one.EUS-CPN is efficacious, should be integrated into the management of pancreas cancer pain, and can be considered early at the time of diagnosis of inoperable disease. Techniques may still vary based on operator experience. Serious complications exist, but are rare.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".